# x.min(axis=1), x.max(axis=1), # color='grey', alpha=.5) #ax.fill_between(np.arange(filter_length), x_min, x_max, # color='red', alpha=.5) #ax.plot(eigvals, 'ok') #%% exp, stimnr = '20180710', 1 ff = Stimulus(exp, stimnr) stimulus = np.array(randpy.gasdev(-1000, ff.frametimings.shape[0])[0]) st = OMB(exp, 8) ff = st stimulus = st.bgsteps[0, :] allspikes = ff.allspikes() i = 0 spikes = allspikes[i, :] filter_length = ff.filter_length rw = asc.rolling_window(stimulus, filter_length, preserve_dim=True) sta = (spikes @ rw) / spikes.sum() #%% # I am not projecting out the STA like Equation 4 in Schwartz et al.2006,J.Vision precovar = (rw * spikes[:, None]) - sta stc = (precovar.T @ precovar) / (spikes.sum() - 1) eigvals, eigvecs = np.linalg.eig(stc) eigsort = np.argsort(eigvals) eigvals, eigvecs = eigvals[eigsort], eigvecs[:, eigsort]
fff_stimnr = asc.stimulisorter(exp)['fff'][0]
fff = Stimulus(exp, fff_stimnr)
fff.filter_length = 20
# Get rid of list of numpy arrays
fff_stas = np.array(fff.read_datafile()['stas'])
glmlabel = 'GLM_contrast_xval'
savepath = os.path.join(fff.stim_dir, glmlabel)
os.makedirs(savepath, exist_ok=True)
fff.get_frametimings()
all_spikes = fff.allspikes()
start = dt.datetime.now()
kall = np.zeros((fff.nclusters, xval_splits, fff.filter_length))
muall = np.zeros((fff.nclusters, xval_splits))
frs = np.zeros((all_spikes.shape[0], int(all_spikes.shape[-1] / xval_splits)))
cross_corrs = np.zeros((fff.nclusters, xval_splits))
t = np.linspace(0, fff.filter_length * fff.frame_duration * 1000,
fff.filter_length)
plotlabels = ['Contrast']